In the area of patient monitoring, movement monitoring can give information about a patient's clinical condition. Dangerous situations could be communicated to the medical staff, such as getting/falling out of bed, pulling of medical equipment (for example the endotracheal tube or feeding tube) or disease specific movements such as grabbing in the air, repetitive movement of legs in the case of delirium, epileptic seizures, etc.
Change in motoric behavior is one of the core features of delirium. Besides changes in overall activity levels (e.g. decreased activity levels for the hypoactive subtype) delirious patients also show unusual movements such as grabbing in the air, picking of the skin or bed sheets, and restless movements of the legs. Movement analysis and movement classification can be of great importance for delirium detection.
In previous studies wrist-worn accelerometer techniques were used to analyze the change in activity levels due to delirium. The on-body wrist sensor may be disturbing or even confusing for the patient. More importantly, it does not capture movements performed by other body parts nor does it provide the possibility for a higher-level interpretation of the movements, such as ‘pinching the skin’, ‘grabbing in the air’. Continuous and automatic video monitoring is believed to offer the opportunity to go beyond these limitations.
In order to recognize/classify patient movements, features of natural and unusual movements are extracted from the images/videos and fed to a classifier. Feature extraction for patient monitoring is commonly performed globally, on the entire body. Yet, movement classification profits from body part information as particular movements are often performed by specific body parts (e.g., moving head continuously from left to right is unusual whereas a repetitive hand movement while eating is not). Thus, the classification outcome improves greatly when features can be extracted per body part.
When patient monitoring with a video camera is performed with the purpose to monitor the vital signs, the chest area (for monitoring breathing) or face area (for monitoring heart rate) is important information.
Thus, for both movement analysis and vital signs monitoring, information on patient region of interest (ROI) and on the location of the main body parts in the image are crucial. This does not only hold for patients, e.g. in a hospital, but generally for all persons, like elderly persons in a nursing home or at their own home, who shall be monitored, or for a child or newborn in an incubator.
In many cases, in the hospital a patient's motoric behavior is mostly only observed when the medical staff visits the patient or sometimes by using checklists. Detection of change in motoric behavior between the past and current visit is often difficult to notice by the medical staff. This type of inspection introduces non-negligible lag in the detection of critical problems, such as the onset of diseases revealed by change in motoric behavior or critical situations induced by the patients' movements.
Other sensors than the video camera are suggested in literature to monitor the patients motoric behavior, however they are often specialized to detect a particular incident (e.g., patient falling out of bed). Video data is rich in information e.g., the possibility to detect patient's face, hands, analyze movements, analyze interaction with objects or recognize general behavior. Therefore, the video sensor offers the opportunity to automatically analyze and recognize different types of movements performed by the patient.
Automatic video-based monitoring of patients is a relatively new topic and the developed tools are at their infancy. The video analysis methods have to cope with the dynamic aspects of the hospital. These can be scene variations such as the changes in the bed angle and bed backrest tilt, persons or objects like the TV screen occluding parts of the patient, different patient lying positions in bed and a blanket covering body parts of the patient and the entrance and the disappearance of the medical personnel and visitors. These challenges make it difficult to include typical body segmentation methods and identification of body parts for patient monitoring. The presence of the blanket makes it difficult to fit a human model on the lying patient; the scene variations limit current video analysis methods for body part segmentation (such as edge/gradient analysis, luminance value analysis, and object detection).
SHYAMSUNDER R ET AL: 11 Compression of Patient Monitoring Video Using Motion Segmentation Technique, JOURNAL OF MEDICAL SYSTEMS. KLUWER ACADEMIC PUBLISHERS-PLENUM PUBLISHERS. NE. vol. 31. no. 2. 21 Mar. 2007 discloses a motion segmentation technique for the separation of stationary and moving portions in a video using a binary mask.
NZ 534482 A discloses methods and systems for objectively determining the level of agitation in a patient. The method involves automated monitoring of physical movement of a defined region of interest of the patient's body, and/or monitoring expert systems that delineate other clinical events from agitation (e.g. atrial fibrillation from large spikes in heart rate due to agitation). Signal processing is performed on physiological signals associated with the monitoring, and changes in the processed signals allow the level of patient agitation to be quantified.
WO 2012/164453 A1 discloses methods and apparatus for monitoring movement and breathing of two or more subjects occupying common bedding. The method comprises the steps of imaging the bedding by an optical sensor; performing a motion estimation by producing motion vectors indicating the local displacement of corresponding image blocks between consecutive images, or images that are several frames apart, received from said optical sensor; calculating motion clusters by measuring spatial and temporal correlations of the motion vectors; and segmenting the calculated motion clusters by assignment of each motion cluster to a corresponding subject, wherein the assignment of the motion clusters to the corresponding subject is based on the spatial and/or temporal similarity of the motion clusters among each other and on previous segmentation results.